Lower-Extremity Segment-Length Prediction Accuracy of the Sanders Multiplier, Paley Multiplier, and White-Menelaus Formula

Lower Extremity Growth according to AI Automated Femorotibial Length Measurement on Slot-Scanning Radiographs in Pediatric Patients

Background Commonly used pediatric lower extremity growth standards are based on small, dated data sets. Artificial intelligence (AI) enables creation of updated growth standards. Purpose To train an AI model using standing slot-scanning radiographs in a racially diverse data set of pediatric patients to measure lower extremity length and to compare expected growth curves derived using AI measurements to those of the conventional Anderson-Green method. Materials and Methods This retrospective study included pediatric patients aged 0-21 years who underwent at least two slot-scanning radiographs in routine clinical care between August 2015 and February 2022. A Mask Region-based Convolutional Neural Network was trained to segment the femur and tibia on radiographs and measure total leg, femoral, and tibial length; accuracy was assessed with mean absolute error. AI measurements were used to create quantile polynomial regression femoral and tibial growth curves, which were compared with the growth curves of the Anderson-Green method for coverage based on the central 90% of the estimated growth distribution. Results In total, 1874 examinations in 523 patients (mean age, 12.7 years ± 2.8 [SD]; 349 female patients) were included; 40% of patients self-identified as White and not Hispanic or Latino, and the remaining 60% self-identified as belonging to a different racial or ethnic group. The AI measurement training, validation, and internal test sets included 114, 25, and 64 examinations, respectively. The mean absolute errors of AI measurements of the femur, tibia, and lower extremity in the test data set were 0.25, 0.27, and 0.33 cm, respectively. All 1874 examinations were used to generate growth curves. AI growth curves more accurately represented lower extremity growth in an external test set (n = 154 examinations) than the Anderson-Green method (90% coverage probability: 86.7% [95% CI: 82.9, 90.5] for AI model vs 73.4% [95% CI: 68.4, 78.3] for Anderson-Green method; χ2 test, P < .001). Conclusion Lower extremity growth curves derived from AI measurements on standing slot-scanning radiographs from a diverse pediatric data set enabled more accurate prediction of pediatric growth. © RSNA, 2024 Supplemental material is available for this article.

Can diffusion tensor imaging unlock the secrets of the growth plate?

"How tall will I be?" Every paediatrician has been asked this during their career. The growth plate is the main site of longitudinal growth of the long bones. The chondrocytes in the growth plate have a columnar pattern detectable by diffusion tensor imaging (DTI). DTI shows the diffusion of water in a tissue and whether it is iso- or anisotropic. By detecting direction and magnitude of diffusion, DTI gives information about the microstructure of the tissue. DTI metrics include tract volume, length, and number, fractional anisotropy (FA), and mean diffusivity. DTI metrics, particularly tract volume, provide quantitative data regarding skeletal growth and, in conjunction with the fractional anisotropy, be used to determine whether a growth plate is normal. Tractography is a visual display of the diffusion, depicting its direction and amplitude. Tractography gives a more qualitative visualization of cellular orientation in a tissue and reflects the activity in the growth plate. These two components of DTI can be used to assess the growth plate without ionizing radiation or pain. Further refinements in DTI will improve prediction of post-imaging growth and growth plate closure, and assessment of the positive and negative effect of treatments like cis-retinoic acid and growth hormone administration.

Comparison of Different Bone Age Methods and Chronological Age in Prediction of Remaining Growth Around the Knee

BACKGROUND

Bone age (BA) has been shown to be superior to chronological age (CA) when predicting remaining growth. However, it is not known whether the calculations are more accurate when BA is assessed by the Greulich and Pyle (GP) or the Sauvegrain (SG) methods. The aim of our study was to identify the method which gives an estimate closest to actual growth in the lower extremities.

METHODS

Leg length radiographs, hand radiographs, and elbow radiographs were simultaneously obtained during the adolescent growth spurt (10 to 16 years) in 52 children treated for LLD, with radiographic follow-up of segmental length (femur, tibia, and foot) until skeletal maturity, were randomly selected from a local institutional register. BA, according to GP and SG, were manually rated, and BA based on the GP method was additionally assessed by the automated BoneXpert (BX) method. The remaining growth was calculated based on the White-Menelaus method for both BA methods (GP, SG), the combination of the 2 methods, GP by BX, CA, and the combination of CA and GP by BX. Estimated growth was compared with the actual growth in the distal femur and proximal tibia from the time of BA determination until skeletal maturity.

RESULTS

For all included methods, the average calculated remaining growth was higher compared with the actual growth. The mean absolute difference between calculated remaining growth and actual growth in the femur and tibia was lowest using GP by BX [0.66 cm (SD 0.51 cm) and 0.43 cm (SD 0.34 cm)] and highest using CA [1.02 (SD 0.72) and 0.67 (SD 0.46)]. It was a significant association between calculated growth and the difference between actual and calculated growth for the SG method ( P =<0.001).

CONCLUSION

During the adolescent growth spurt, the GP method compared with the SG method and CA gives the most accurate estimate of remaining growth around the knee according to our results.

CLINICAL RELEVANCE

In calculations of remaining growth around the knee, BA assessment by the GP atlas or BX method should be used as the parameter of biological maturity.

Comparison of "Human" and Artificial Intelligence Hand-and-Wrist Skeletal Age Estimation in an Epiphysiodesis Cohort

BACKGROUND

We previously demonstrated that the White-Menelaus arithmetic formula combined with skeletal age as estimated with the Greulich and Pyle (GP) atlas was the most accurate method for predicting leg lengths and residual leg-length discrepancy (LLD) at maturity in a cohort of patients treated with epiphysiodesis. We sought to determine if an online artificial intelligence (AI)-based hand-and-wrist skeletal age system provided consistent readings and to evaluate how these readings influenced the prediction of the outcome of epiphysiodesis in this cohort.

METHODS

JPEG images of perioperative hand radiographs for 76 subjects were independently submitted by 2 authors to an AI skeletal age web site (http://physis.16bit.ai/). We compared the accuracy of the predicted long-leg length (after epiphysiodesis), short-leg length, and residual LLD with use of the White-Menelaus formula and either human-estimated GP or AI-estimated skeletal age.

RESULTS

The AI skeletal age readings had an intraclass correlation coefficient (ICC) of 0.99. AI-estimated skeletal age was generally greater than human-estimated GP skeletal age (average, 0.5 year greater in boys and 0.1 year greater in girls). Overall, the prediction accuracy was improved with AI readings; these differences reached significance for the short-leg and residual LLD prediction errors. Residual LLD was underestimated by ≥1.0 cm in 26 of 76 subjects when human-estimated GP skeletal age was used (range of underestimation, 1.0 to 3.2 cm), compared with only 10 of 76 subjects when AI skeletal age was used (range of underestimation, 1.1 cm to 2.2 cm) (p < 0.01). Residual LLD was overestimated by ≥1.0 cm in 3 of 76 subjects by both methods (range of overestimation, 1.0 to 1.3 cm for the human-estimated GP method and 1.0 to 1.6 cm for the AI method).

CONCLUSIONS

The AI method of determining hand-and-wrist skeletal age was highly reproducible in this cohort and improved the accuracy of prediction of leg length and residual discrepancy when compared with traditional human interpretation of the GP atlas. This improvement could be explained by more accurate estimation of skeletal age via a machine-learning AI system calibrated with a large database.

LEVEL OF EVIDENCE

Prognostic Level III. See Instructions for Authors for a complete description of levels of evidence.

Intra- and interobserver reliability analysis of pediatric lower limb parameters on digital long leg radiographs

BACKGROUND

Malalignments of the lower extremity are common reasons for orthopedic consultation because it may lead to osteoarthritis in adulthood. An accurate and reliable radiological assessment of lower limb alignment in children and adolescents is essential for clinical decision-making on treatment of limb deformities and for regular control after a surgical intervention.

OBJECTIVE

First, does the analysis of full-length standing anteroposterior radiographs show a good intra- and interobserver reliability? Second, which parameter is most susceptible to observer-dependent errors? Third, what is the Standard Error of Measurement (SEM_95%) of the absolute femoral and tibial length?

METHODS

Two observers evaluated digital radiographs of 144 legs from 36 children and adolescents with pathological valgus alignment before a temporary hemiepiphysiodesis and before implant removal. Parameters included Mechanical Femorotibial Angle (MFA), Mechanical Axis Deviation (MAD), mechanical Lateral Distal Femoral Angle (mLDFA), mechanical Medial Proximal Tibial Angle (mMPTA), mechanical Lateral Proximal Femoral Angle (mLPFA), mechanical Lateral Distal Tibial Angle (mLDTA), Joint Line Convergence Angle (JLCA), femur length, tibial length. Intra- and interobserver reliability (ICC_2,1), SEM_95% and proportional errors were calculated.

RESULTS

The intra- and interobserver reliability for almost all measurements was found to be good to excellent (Intra-ICC_2,1: 0.849-0.999; Inter-ICC_2,1: 0.864-0.996). The SEM_95% of both observers was found to be ± 1.39° (MFA), ± 3.31 mm (MAD), ± 1.06° (mLDFA) and ± 1.29° (mMPTA). The proportional error of MAD and MFA is comparable (47.29% vs. 46.33%). The relevant knee joint surface angles show a lower proportional error for mLDFA (42.40%) than for mMPTA (51.60%). JLCA has a proportional error of 138%. Furthermore, the SEM_95% for the absolute values of the femoral and tibial length was 4.53 mm for the femur and 3.12 mm for the tibia.

CONCLUSIONS

In conclusion, a precise malalignment measurement and the knowledge about SEM_95% of the respective parameters are crucial for correct surgical or nonsurgical treatment. The susceptibility to error must be considered when interpreting malalignment analysis and must be considered when planning a surgical intervention. The results of the present study elucidate that MAD and MFA are equally susceptible to observer-dependent errors. This study shows good to excellent intra- and interobserver ICCs for all leg alignment parameters and joint surface angles, except for JLCA.

TRIAL REGISTRATION

This study was registered with DRKS (German Clinical Trials Register) under the number DRKS00015053.

LEVEL OF EVIDENCE

I, Diagnostic Study.

Indications and Timing of Guided Growth Techniques for Pediatric Upper Extremity Deformities: A Literature Review

Osseous deformities in children arise due to progressive angular growth or complete physeal arrest. Clinical and radiological alignment measurements help to provide an impression of the deformity, which can be corrected using guided growth techniques. However, little is known about timing and techniques for the upper extremity. Treatment options for deformity correction include monitoring of the deformity, (hemi-)epiphysiodesis, physeal bar resection, and correction osteotomy. Treatment is dependent on the extent and location of the deformity, physeal involvement, presence of a physeal bar, patient age, and predicted length inequality at skeletal maturity. An accurate estimation of the projected limb or bone length inequality is crucial for optimal timing of the intervention. The Paley multiplier method remains the most accurate and simple method for calculating limb growth. While the multiplier method is accurate for calculating growth prior to the growth spurt, measuring peak height velocity (PHV) is superior to chronological age after the onset of the growth spurt. PHV is closely related to skeletal age in children. The Sauvegrain method of skeletal age assessment using elbow radiographs is possibly a simpler and more reliable method than the method by Greulich and Pyle using hand radiographs. PHV-derived multipliers need to be developed for the Sauvegrain method for a more accurate calculation of limb growth during the growth spurt. This paper provides a review of the current literature on the clinical and radiological evaluation of normal upper extremity alignment and aims to provide state-of-the-art directions on deformity evaluation, treatment options, and optimal timing of these options during growth.